import fundamentalanalysis as fa
import pandas as pd
import numpy as np
tickers = ["AAPL","META","GOOG","V","MSFT","AMZN","ZTS", "SWKS", "GPC", "LRCX", "CHD", "WBA", "XYL", "AIZ", "PGR", "HSY", "AMT", "NWSA", "EW", "MU", "CCL", "AEP", "AES", "TROW", "FE","VRTX", "WAT", "IP", "PEAK", "BKNG", "CDW", "CCI", "FANG", "LUV", "HUM", "IRM", "GWW", "HWM", "LYB", "PSX", "MRO", "DISCA", "ZBH"]
api_key = "2ccc6ec16a686c78ee04299bd35bc09a"
key_metrics_annually = []
for ticker in tickers:
key_metrics = fa.key_metrics(ticker, api_key)
key_metrics_annually.append(key_metrics)
# Combine the results into a single dataframe
df = pd.concat(key_metrics_annually, keys=tickers)
dta_ptb_pairs = []
for ticker in tickers:
key_metrics = fa.key_metrics(ticker, api_key, period="annual")
y = key_metrics.iloc[25, 1] # Debt to Assets
x = key_metrics.iloc[17, 1] # Price to Book
dta_ptb_pairs.append([x, y])
df2 = pd.DataFrame(dta_ptb_pairs, index=tickers, columns=["Debt to Assets", "Price to Book"])
df2
| Debt to Assets | Price to Book | |
|---|---|---|
| AAPL | 38.892865 | 0.820257 |
| META | 7.581941 | 0.247658 |
| GOOG | 7.677408 | 0.299590 |
| V | 12.628169 | 0.546552 |
| MSFT | 14.398979 | 0.574605 |
| AMZN | 12.204246 | 0.671275 |
| ZTS | 25.479890 | 0.673094 |
| SWKS | 5.112153 | 0.383391 |
| GPC | 5.760835 | 0.755904 |
| LRCX | 10.022539 | 0.634886 |
| CHD | 7.763903 | 0.595673 |
| WBA | 1.842356 | 0.706932 |
| XYL | 6.716973 | 0.610198 |
| AIZ | 1.679089 | 0.838117 |
| PGR | 3.290930 | 0.743694 |
| HSY | 14.506168 | 0.735193 |
| AMT | 25.990547 | 0.870226 |
| NWSA | 1.852954 | 0.454654 |
| EW | 13.836515 | 0.313633 |
| MU | 1.886254 | 0.253462 |
| CCL | 1.629252 | 0.772327 |
| AEP | 1.985069 | 0.741296 |
| AES | 5.784060 | 0.861451 |
| TROW | 4.938502 | 0.180185 |
| FE | 2.612859 | 0.809055 |
| VRTX | 5.603061 | 0.248092 |
| WAT | 62.420338 | 0.881240 |
| IP | 2.014315 | 0.640217 |
| PEAK | 2.985200 | 0.537359 |
| BKNG | 15.938685 | 0.738674 |
| CDW | 40.189925 | 0.946535 |
| CCI | 10.919797 | 0.788473 |
| FANG | 1.576021 | 0.421565 |
| LUV | 2.435306 | 0.713271 |
| HUM | 3.712265 | 0.636854 |
| IRM | 17.673376 | 0.940687 |
| GWW | 12.452156 | 0.672330 |
| HWM | 3.901625 | 0.656718 |
| LYB | 2.597809 | 0.676882 |
| PSX | 1.663593 | 0.610803 |
| MRO | 1.209296 | 0.371190 |
| DISCA | 1.452165 | 0.647960 |
| ZBH | 2.093134 | 0.460002 |
import plotly.express as px
fig = px.scatter(df2,x="Price to Book", y ="Debt to Assets")
fig.show()